FriendliAI Container.Completions API

openapi: 3.2.0
info:
  title: Friendli Suite API Reference Container.Audio Container.Completions API
  description: This is an OpenAPI reference of Friendli Suite API.
  termsOfService: https://friendli.ai/terms-of-service
  contact:
    name: FriendliAI Support Team
    email: support@friendli.ai
  version: 0.1.0
servers:
- url: https://api.friendli.ai
tags:
- name: Container.Completions
paths:
  /v1/completions:
    servers:
    - url: http://localhost:8000
    post:
      tags:
      - Container.Completions
      summary: Completions
      description: Generate a text completion from a prompt or token sequence. Supports streaming and configurable generation parameters.
      operationId: containerCompletionsComplete
      requestBody:
        content:
          application/json:
            schema:
              $ref: '#/components/schemas/ContainerCompletionsBody'
        required: true
      responses:
        '200':
          description: Successfully generated a completion response.
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/ContainerCompletionsSuccess'
              examples:
                Example:
                  value:
                    id: cmpl-26a1e10db8544bc3adb488d2d205288b
                    object: text_completion
                    choices:
                    - index: 0
                      seed: 42
                      text: This is indeed a test
                      tokens:
                      - 128000
                      - 2028
                      - 374
                      - 13118
                      - 264
                      - 1296
                      finish_reason: stop
                    usage:
                      prompt_tokens: 7
                      completion_tokens: 6
                      total_tokens: 13
        '422':
          description: Unprocessable Entity
      x-speakeasy-name-override: complete
      x-mint:
        metadata:
          title: Container Completions
          sidebarTitle: Completions
          og:title: Container Completions
          description: Generate a text completion from a prompt or token sequence. Supports streaming and configurable generation parameters.
          og:description: Generate a text completion from a prompt or token sequence. Supports streaming and configurable generation parameters.
        href: /openapi/container/completions
        content: 'Generate a text completion from a prompt or token sequence. Supports streaming and configurable generation parameters.


          When streaming mode is used (i.e., `stream` option is set to `true`), the response is in MIME type `text/event-stream`. Otherwise, the content type is `application/json`.

          You can view the schema of the streamed sequence of chunk objects in streaming mode [here](/openapi/container/completions-chunk-object).'
  /v1/completions#stream:
    servers:
    - url: http://localhost:8000
    post:
      tags:
      - Container.Completions
      summary: Stream completions
      description: Generate a text completion from a prompt or token sequence. Supports streaming and configurable generation parameters.
      operationId: containerCompletionsStream
      requestBody:
        content:
          application/json:
            schema:
              $ref: '#/components/schemas/ContainerCompletionsStreamBody'
        required: true
      responses:
        '200':
          description: Successfully generated a completion response.
          content:
            text/event-stream:
              examples:
                Example:
                  value: 'data: {"id":"cmpl-26a1e10db8544bc3adb488d2d205288b","object":"text_completion","choices":[{"index":0,"text":" such","token":1778,"finish_reason":null,"logprobs":null}],"created":1733382157}


                    data: {"id":"cmpl-26a1e10db8544bc3adb488d2d205288b","object":"text_completion","choices":[{"index":0,"text":" as","token":439,"finish_reason":null,"logprobs":null}],"created":1733382157}


                    ...


                    data: {"id":"cmpl-26a1e10db8544bc3adb488d2d205288b","object":"text_completion","choices":[{"index":0,"text":"","finish_reason":"length","logprobs":null}],"created":1733382157}


                    data: {"id":"cmpl-26a1e10db8544bc3adb488d2d205288b","object":"text_completion","choices":[],"usage":{"prompt_tokens":5,"completion_tokens":10,"total_tokens":15},"created":1733382157}


                    data: [DONE]

                    '
              x-speakeasy-sse-sentinel: '[DONE]'
              schema:
                $ref: '#/components/schemas/ContainerCompletionsStreamSuccess'
        '422':
          description: Unprocessable Entity
      x-speakeasy-name-override: stream
      x-mint:
        metadata:
          title: Container Stream Completions
          sidebarTitle: Stream Completions
          og:title: Container Stream Completions
          description: Generate a text completion from a prompt or token sequence. Supports streaming and configurable generation parameters.
          og:description: Generate a text completion from a prompt or token sequence. Supports streaming and configurable generation parameters.
        content: 'Generate a text completion from a prompt or token sequence. Supports streaming and configurable generation parameters.


          When streaming mode is used (i.e., `stream` option is set to `true`), the response is in MIME type `text/event-stream`. Otherwise, the content type is `application/json`.

          You can view the schema of the streamed sequence of chunk objects in streaming mode [here](/openapi/container/completions-chunk-object).'
components:
  schemas:
    TextUsage:
      properties:
        prompt_tokens:
          type: integer
          title: Prompt Tokens
          description: Number of tokens in the prompt.
          examples:
          - 5
        completion_tokens:
          type: integer
          title: Completion Tokens
          description: Number of tokens in the generated completions.
          examples:
          - 7
        total_tokens:
          type: integer
          title: Total Tokens
          description: Total number of tokens used in the request (`prompt_tokens` + `completion_tokens`).
          examples:
          - 12
        prompt_tokens_details:
          anyOf:
          - $ref: '#/components/schemas/PromptTokensDetails'
          - type: 'null'
          description: Breakdown of tokens used in the prompt.
      type: object
      required:
      - prompt_tokens
      - completion_tokens
      - total_tokens
      title: TextUsage
    PromptTokensDetails:
      properties:
        cached_tokens:
          anyOf:
          - type: integer
          - type: 'null'
          title: Cached Tokens
          description: Cached tokens present in the prompt.
      type: object
      title: PromptTokensDetails
    ResponseFormat:
      oneOf:
      - $ref: '#/components/schemas/ResponseFormatJsonSchema'
        title: Json Schema
      - $ref: '#/components/schemas/ResponseFormatJsonObject'
        title: Json Object
      - $ref: '#/components/schemas/ResponseFormatRegex'
        title: Regex
      - $ref: '#/components/schemas/ResponseFormatText'
        title: Text
      description: 'The enforced format of the model''s output.


        Note that the content of the output message may be truncated if it exceeds the `max_tokens`. You can check this by verifying that the `finish_reason` of the output message is `length`.


        For more detailed information, please refer [here](https://friendli.ai/docs/guides/structured-outputs).


        ***Important***

        You must explicitly instruct the model to produce the desired output format using a system prompt or user message (e.g., `You are an API generating a valid JSON as output.`).

        Otherwise, the model may result in an unending stream of whitespace or other characters.


        **When `response_format` is specified, `min_tokens` field is unsupported.**'
      discriminator:
        propertyName: type
        mapping:
          json_object: '#/components/schemas/ResponseFormatJsonObject'
          json_schema: '#/components/schemas/ResponseFormatJsonSchema'
          regex: '#/components/schemas/ResponseFormatRegex'
          text: '#/components/schemas/ResponseFormatText'
    StreamedCompletionsChoice:
      properties:
        index:
          type: integer
          title: Index
          description: The index of the choice in the list of generated choices.
          examples:
          - 0
        text:
          type: string
          title: Text
          description: Generated text output.
          examples:
          - This is indeed a test
        logprobs:
          anyOf:
          - $ref: '#/components/schemas/CompletionsLogprobs'
          - type: 'null'
        finish_reason:
          anyOf:
          - type: string
            enum:
            - stop
            - length
            - 'null'
          - type: 'null'
          title: Finish Reason
          description: Termination condition of the generation. `stop` means the API returned the full completions generated by the model without running into any limits. `length` means the generation exceeded `max_tokens` or the conversation exceeded the max context length.
        token:
          anyOf:
          - type: integer
          - type: 'null'
          title: Token
          description: The token.
      type: object
      required:
      - index
      - text
      title: StreamedCompletionsChoice
    CompletionsBodyWithPrompt:
      properties:
        model:
          anyOf:
          - type: string
          - type: 'null'
          title: Model
          description: Routes the request to a specific adapter.
          examples:
          - (adapter-route)
        bad_word_tokens:
          anyOf:
          - items:
              $ref: '#/components/schemas/TokenSequence'
            type: array
          - type: 'null'
          title: Bad Word Tokens
          description: Same as the above `bad_words` field, but receives token sequences instead of text phrases. This is similar to Hugging Face's [`bad_word_ids`](https://huggingface.co/docs/transformers/v4.26.0/en/main_classes/text_generation#transformers.GenerationConfig.bad_words_ids) argument.
        bad_words:
          anyOf:
          - items:
              type: string
            type: array
          - type: 'null'
          title: Bad Words
          description: 'Text phrases that should not be generated.

            For a bad word phrase that contains N tokens, if the first N-1 tokens appears at the last of the generated result, the logit for the last token of the phrase is set to -inf.

            Before checking whether a bard word is included in the result, the word is converted into tokens.

            We recommend using `bad_word_tokens` because it is clearer.

            For example, after tokenization, phrases "clear" and " clear" can result in different token sequences due to the prepended space character.

            Defaults to empty list.

            '
        embedding_to_replace:
          anyOf:
          - items:
              type: number
            type: array
          - type: 'null'
          title: Embedding To Replace
          description: A list of flattened embedding vectors used for replacing the tokens at the specified indices provided via `token_index_to_replace`.
        encoder_no_repeat_ngram:
          anyOf:
          - type: integer
          - type: 'null'
          title: Encoder No Repeat Ngram
          description: If this exceeds 1, every ngram of that size occurring in the input token sequence cannot appear in the generated result. 1 means that this mechanism is disabled (i.e., you cannot prevent 1-gram from being generated repeatedly). Only allowed for encoder-decoder models. Defaults to 1. This is similar to Hugging Face's [`encoder_no_repeat_ngram_size`](https://huggingface.co/docs/transformers/v4.26.0/en/main_classes/text_generation#transformers.GenerationConfig.encoder_no_repeat_ngram_size) argument.
        encoder_repetition_penalty:
          anyOf:
          - type: number
          - type: 'null'
          title: Encoder Repetition Penalty
          description: Penalizes tokens that have already appeared in the input tokens. Should be positive value. 1.0 means no penalty. Only allowed for encoder-decoder models. See [Keskar et al., 2019](https://arxiv.org/abs/1909.05858) for more details. This is similar to Hugging Face's [`encoder_repetition_penalty`](https://huggingface.co/docs/transformers/v4.26.0/en/main_classes/text_generation#transformers.GenerationConfig.encoder_repetition_penalty) argument.
        eos_token:
          anyOf:
          - items:
              type: integer
            type: array
          - type: 'null'
          title: Eos Token
          description: A list of endpoint sentence tokens.
        forced_output_tokens:
          anyOf:
          - items:
              type: integer
            type: array
          - type: 'null'
          title: Forced Output Tokens
          description: A token sequence that is enforced as a generation output. This option can be used when evaluating the model for the datasets with multi-choice problems (e.g., [HellaSwag](https://huggingface.co/datasets/hellaswag), [MMLU](https://huggingface.co/datasets/cais/mmlu)). Use this option with `logprobs` to get logprobs for the evaluation.
        frequency_penalty:
          anyOf:
          - type: number
          - type: 'null'
          title: Frequency Penalty
          description: Number between -2.0 and 2.0. Positive values penalizes tokens that have been sampled, taking into account their frequency in the preceding text. This penalization diminishes the model's tendency to reproduce identical lines verbatim.
        logprobs:
          anyOf:
          - type: integer
          - type: 'null'
          title: Logprobs
          description: Include the log probabilities on the logprobs most likely output tokens, as well the chosen tokens.
        max_tokens:
          anyOf:
          - type: integer
          - type: 'null'
          title: Max Tokens
          description: The maximum number of tokens to generate. For decoder-only models like GPT, the length of your input tokens plus `max_tokens` should not exceed the model's maximum length (e.g., 2048 for OpenAI GPT-3). For encoder-decoder models like T5 or BlenderBot, `max_tokens` should not exceed the model's maximum output length. This is similar to Hugging Face's [`max_new_tokens`](https://huggingface.co/docs/transformers/v4.26.0/en/main_classes/text_generation#transformers.GenerationConfig.max_new_tokens) argument.
        max_total_tokens:
          anyOf:
          - type: integer
          - type: 'null'
          title: Max Total Tokens
          description: The maximum number of tokens including both the generated result and the input tokens. Only allowed for decoder-only models. Only one argument between `max_tokens` and `max_total_tokens` is allowed. Default value is the model's maximum length. This is similar to Hugging Face's [`max_length`](https://huggingface.co/docs/transformers/v4.26.0/en/main_classes/text_generation#transformers.GenerationConfig.max_length) argument.
        min_p:
          anyOf:
          - type: number
          - type: 'null'
          title: Min P
          description: A scaling factor used to determine the minimum token probability threshold. This threshold is calculated as `min_p` multiplied by the probability of the most likely token. Tokens with probabilities below this scaled threshold are excluded from sampling. Values range from 0.0 (inclusive) to 1.0 (inclusive). Higher values result in stricter filtering, while lower values allow for greater diversity. The default value of 0.0 disables filtering, allowing all tokens to be considered for sampling.
        min_tokens:
          anyOf:
          - type: integer
          - type: 'null'
          title: Min Tokens
          description: 'The minimum number of tokens to generate. Default value is 0. This is similar to Hugging Face''s [`min_new_tokens`](https://huggingface.co/docs/transformers/v4.26.0/en/main_classes/text_generation#transformers.generationconfig.min_new_tokens) argument.


            **This field is unsupported when `response_format` is specified.**'
        min_total_tokens:
          anyOf:
          - type: integer
          - type: 'null'
          title: Min Total Tokens
          description: The minimum number of tokens including both the generated result and the input tokens. Only allowed for decoder-only models. Only one argument between `min_tokens` and `min_total_tokens` is allowed. This is similar to Hugging Face's [`min_length`](https://huggingface.co/docs/transformers/v4.26.0/en/main_classes/text_generation#transformers.GenerationConfig.min_length) argument.
        n:
          anyOf:
          - type: integer
          - type: 'null'
          title: N
          description: The number of independently generated results for the prompt. Defaults to 1. This is similar to Hugging Face's [`num_return_sequences`](https://huggingface.co/docs/transformers/v4.26.0/en/main_classes/text_generation#transformers.GenerationConfig.num_return_sequences) argument.
        no_repeat_ngram:
          anyOf:
          - type: integer
          - type: 'null'
          title: No Repeat Ngram
          description: If this exceeds 1, every ngram of that size can only occur once among the generated result (plus the input tokens for decoder-only models). 1 means that this mechanism is disabled (i.e., you cannot prevent 1-gram from being generated repeatedly). Defaults to 1. This is similar to Hugging Face's [`no_repeat_ngram_size`](https://huggingface.co/docs/transformers/v4.26.0/en/main_classes/text_generation#transformers.GenerationConfig.no_repeat_ngram_size) argument.
        presence_penalty:
          anyOf:
          - type: number
          - type: 'null'
          title: Presence Penalty
          description: Number between -2.0 and 2.0. Positive values penalizes tokens that have been sampled at least once in the existing text.
        repetition_penalty:
          anyOf:
          - type: number
          - type: 'null'
          title: Repetition Penalty
          description: Penalizes tokens that have already appeared in the generated result (plus the input tokens for decoder-only models). Should be positive value (1.0 means no penalty). See [keskar et al., 2019](https://arxiv.org/abs/1909.05858) for more details. This is similar to Hugging Face's [`repetition_penalty`](https://huggingface.co/docs/transformers/v4.26.0/en/main_classes/text_generation#transformers.generationconfig.repetition_penalty) argument.
        response_format:
          anyOf:
          - $ref: '#/components/schemas/ResponseFormat'
          - type: 'null'
        seed:
          anyOf:
          - items:
              type: integer
            type: array
          - type: integer
          - type: 'null'
          title: Seed
          description: Seed to control random procedure. If nothing is given, the API generate the seed randomly, use it for sampling, and return the seed along with the generated result. When using the `n` argument, you can pass a list of seed values to control all of the independent generations.
        stop:
          anyOf:
          - items:
              type: string
            type: array
          - type: 'null'
          title: Stop
          description: 'When one of the stop phrases appears in the generation result, the API will stop generation.

            The stop phrases are excluded from the result.

            Defaults to empty list.'
        stop_tokens:
          anyOf:
          - items:
              $ref: '#/components/schemas/TokenSequence'
            type: array
          - type: 'null'
          title: Stop Tokens
          description: Stop generating further tokens when generated token corresponds to any of the tokens in the sequence.
        stream:
          anyOf:
          - type: boolean
          - type: 'null'
          title: Stream
          description: Whether to stream the generation result. When set to `true`, each token is sent as [server-sent events](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#event_stream_format) once generated.
          default: false
        stream_options:
          anyOf:
          - $ref: '#/components/schemas/StreamOptions'
          - type: 'null'
          description: 'Options related to stream.

            It can only be used when `stream: true`.'
        temperature:
          anyOf:
          - type: number
          - type: 'null'
          title: Temperature
          description: Sampling temperature. Smaller temperature makes the generation result closer to greedy, argmax (i.e., `top_k = 1`) sampling. Defaults to 1.0. This is similar to Hugging Face's [`temperature`](https://huggingface.co/docs/transformers/v4.26.0/en/main_classes/text_generation#transformers.generationconfig.temperature) argument.
        token_index_to_replace:
          anyOf:
          - items:
              type: integer
            type: array
          - type: 'null'
          title: Token Index To Replace
          description: A list of token indices where to replace the embeddings of input tokens provided via either `tokens` or `prompt`.
        top_k:
          anyOf:
          - type: integer
          - type: 'null'
          title: Top K
          description: Limits sampling to the top k tokens with the highest probabilities. Values range from 0 (no filtering) to the model's vocabulary size (inclusive). The default value of 0 applies no filtering, allowing all tokens.
          examples:
          - 1
        top_p:
          anyOf:
          - type: number
          - type: 'null'
          title: Top P
          description: Keeps only the smallest set of tokens whose cumulative probabilities reach `top_p` or higher. Values range from 0.0 (exclusive) to 1.0 (inclusive). The default value of 1.0 includes all tokens, allowing maximum diversity.
        xtc_threshold:
          anyOf:
          - type: number
          - type: 'null'
          title: Xtc Threshold
          description: A probability threshold used to identify “top choice” tokens for exclusion in XTC (Exclude Top Choices) sampling. Tokens with probabilities at or above this threshold are considered viable candidates, and all but the least likely viable token are excluded from sampling. This option reduces the dominance of highly probable tokens while preserving some diversity by keeping the least confident “top choice.” Values range from 0.0 (inclusive) to 1.0 (inclusive). Higher values make the filtering more selective by requiring higher probabilities to trigger exclusion, while lower values apply filtering more broadly. The default value of 0.0 disables XTC filtering entirely.
        xtc_probability:
          anyOf:
          - type: number
          - type: 'null'
          title: Xtc Probability
          description: The probability that XTC (Exclude Top Choices) filtering will be applied for each sampling decision. When XTC is triggered, high-probability tokens above the `xtc_threshold` are excluded except for the least likely viable token. This stochastic activation allows for a balance between standard sampling and creativity-boosting exclusion filtering. Values range from 0.0 (inclusive) to 1.0 (inclusive), where 0.0 means XTC is never applied, 1.0 means XTC is always applied when viable tokens exist, and intermediate values provide probabilistic activation. The default value of 0.0 disables XTC filtering.
        prompt:
          anyOf:
          - type: string
          - items:
              type: string
            type: array
          title: Prompt
          description: The prompt (i.e., input text) to generate completions for. Either `prompt` or `tokens` field is required.
          examples:
          - Say this is a test!
      type: object
      required:
      - prompt
      title: CompletionsBodyWithPrompt
    StreamedCompletionsResult:
      properties:
        data:
          $ref: '#/components/schemas/StreamedCompletionData'
      type: object
      required:
      - data
      title: StreamedCompletionsResult
    ResponseFormatJsonObject:
      properties:
        type:
          type: string
          const: json_object
          title: Type
          description: 'The type of the response format: `json_object`'
      type: object
      required:
      - type
      title: ResponseFormatJsonObject
    TokenSequence:
      properties:
        tokens:
          anyOf:
          - items:
              type: integer
            type: array
          - type: 'null'
          title: Tokens
          description: A List of token IDs.
      type: object
      title: TokenSequence
    CompletionsLogprobs:
      properties:
        text_offset:
          items:
            type: integer
          type: array
          title: Text Offset
          description: The starting character position of each token in the generated text, useful for mapping tokens back to their exact location for detailed analysis.
        token_logprobs:
          items:
            type: number
          type: array
          title: Token Logprobs
          description: The log probabilities of each generated token, indicating the model's confidence in selecting each token.
        tokens:
          items:
            type: string
          type: array
          title: Tokens
          description: A list of individual tokens generated in the completion, representing segments of text such as words or pieces of words.
        top_logprobs:
          items:
            additionalProperties: true
            type: object
          type: array
          title: Top Logprobs
          description: A list of dictionaries, where each dictionary represents the top alternative tokens considered by the model at a specific position in the generated text, along with their log probabilities. The number of items in each dictionary matches the value of `logprobs`.
      type: object
      required:
      - text_offset
      - token_logprobs
      - tokens
      - top_logprobs
      title: CompletionsLogprobs
    CompletionsStreamBodyWithPrompt:
      properties:
        model:
          anyOf:
          - type: string
          - type: 'null'
          title: Model
          description: Routes the request to a specific adapter.
          examples:
          - (adapter-route)
        bad_word_tokens:
          anyOf:
          - items:
              $ref: '#/components/schemas/TokenSequence'
            type: array
          - type: 'null'
          title: Bad Word Tokens
          description: Same as the above `bad_words` field, but receives token sequences instead of text phrases. This is similar to Hugging Face's [`bad_word_ids`](https://huggingface.co/docs/transformers/v4.26.0/en/main_classes/text_generation#transformers.GenerationConfig.bad_words_ids) argument.
        bad_words:
          anyOf:
          - items:
              type: string
            type: array
          - type: 'null'
          title: Bad Words
          description: 'Text phrases that should not be generated.

            For a bad word phrase that contains N tokens, if the first N-1 tokens appears at the last of the generated result, the logit for the last token of the phrase is set to -inf.

            Before checking whether a bard word is included in the result, the word is converted into tokens.

            We recommend using `bad_word_tokens` because it is clearer.

            For example, after tokenization, phrases "clear" and " clear" can result in different token sequences due to the prepended space character.

            Defaults to empty list.

            '
        embedding_to_replace:
          anyOf:
          - items:
              type: number
            type: array
          - type: 'null'
          title: Embedding To Replace
          description: A list of flattened embedding vectors used for replacing the tokens at the specified indices provided via `token_index_to_replace`.
        encoder_no_repeat_ngram:
          anyOf:
          - type: integer
          - type: 'null'
          title: Encoder No Repeat Ngram
          description: If this exceeds 1, every ngram of that size occurring in the input token sequence cannot appear in the generated result. 1 means that this mechanism is disabled (i.e., you cannot prevent 1-gram from being generated repeatedly). Only allowed for encoder-decoder models. Defaults to 1. This is similar to Hugging Face's [`encoder_no_repeat_ngram_size`](https://huggingface.co/docs/transformers/v4.26.0/en/main_classes/text_generation#transformers.GenerationConfig.encoder_no_repeat_ngram_size) argument.
        encoder_repetition_penalty:
          anyOf:
          - type: number
          - type: 'null'
          title: Encoder Repetition Penalty
          description: Penalizes tokens that have already appeared in the input tokens. Should be positive value. 1.0 means no penalty. Only allowed for encoder-decoder models. See [Keskar et al., 2019](https://arxiv.org/abs/1909.05858) for more details. This is similar to Hugging Face's [`encoder_repetition_penalty`](https://huggingface.co/docs/transformers/v4.26.0/en/main_classes/text_generation#transformers.GenerationConfig.encoder_repetition_penalty) argument.
        eos_token:
          anyOf:
          - items:
              type: integer
            type: array
          - type: 'null'
          title: Eos Token
          description: A list of endpoint sentence tokens.
        forced_output_tokens:
          anyOf:
          - items:
              type: integer
            type: array
          - type: 'null'
          title: Forced Output Tokens
          description: A token sequence that is enforced as a generation output. This option can be used when evaluating the model for the datasets with multi-choice problems (e.g., [HellaSwag](https://huggingface.co/datasets/hellaswag), [MMLU](https://huggingface.co/datasets/cais/mmlu)). Use this option with `logprobs` to get logprobs for the evaluation.
        frequency_penalty:
          anyOf:
          - type: number
          - type: 'null'
          title: Frequency Penalty
          description: Number between -2.0 and 2.0. Positive values penalizes tokens that have been sampled, taking into account their frequency in the preceding text. This penalization diminishes the model's tendency to reproduce identical lines verbatim.
        logprobs:
          anyOf:
          - type: integer
          - type: 'null'
          title: Logprobs
          description: Include the log probabilities on the logprobs most likely output tokens, as well the chosen tokens.
        max_tokens:
          anyOf:
          - type: integer
          - type: 'null'
          title: Max Tokens
          description: The maximum number of tokens to generate. For decoder-only models like GPT, the length of your input tokens plus `max_tokens` should not exceed the model's maximum length (e.g., 2048 for OpenAI GPT-3). For encoder-decoder models like T5 or BlenderBot, `max_tokens` should not exceed the model's maximum output length. This is similar to Hugging Face's [`max_new_tokens`](https://huggingface.co/docs/transformers/v4.26.0/en/main_classes/text_generation#transformers.GenerationConfig.max_new_tokens) argument.
        max_total_tokens:
          anyOf:
          - type: integer
          - type: 'null'
          title: Max Total Tokens
          description: The maximum number of tokens including both the generated result and the input tokens. Only allowed for decoder-only models. Only one argument between `max_tokens` and `max_total_tokens` is allowed. Default value is the model's maximum length. This is similar to Hugging Face's [`max_length`](https://huggingface.co/docs/transformers/v4.26.0/en/main_classes/text_generation#transformers.GenerationConfig.max_length) argument.
        min_p:
          anyOf:
          - type: number
          - type: 'null'
          title: Min P
          description: A scaling factor used to determine the minimum token probability threshold. This threshold is calculated as `min_p` multiplied by the probability of the most likely token. Tokens with probabilities below this scaled threshold are excluded from sampling. Values range from 0.0 (inclusive) to 1.0 (incl

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